asasim: Adaptive sampling for electromagnetic simulations

Kristian Tølbøl Sørensen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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For simulations of electromagnetic resonance spectra, where the locations of spectral features are unknown, and for wide-band simulations in general, a substantial number of wavelengths must be simulated for acceptable resolution, increasing computation time. This problem is exacerbated for spectra containing narrow-band features, as a high spectral resolution is required to even detect them. To address this challenge, a heuristic algorithm is presented for electromagnetic simulations, which adaptively refines the local resolution of spectral features during a simulation. The method supports parallel processing and plugs in with existing simulation systems, such as (RCWA). It can routinely reduce the computational load by two orders of magnitude.

Program Summary:

Program Title: asasim

Program Files doi:

Licensing provisions: CC By 4.0

Programming language: MATLAB

Nature of problem: Simulations are challenging when information is needed both on a long scale (broad interval) and on a short scale (high local resolution), such as wide-band electromagnetic spectra containing narrow-band features. When resolution is insufficient, narrow-band features may be downright absent from the spectrum, if neighboring points are simulated on either side of a narrow peak. When local resolution is sufficient, it will necessarily be excessive in flat regions, wastefully increasing computation time.

Solution method: The presented method enables adaptive resolution, which ensures that all peaks of a given minimum width are always detected and maximally resolved, while feature-less regions remain minimally resolved. An optimum point spacing is derived for lorentzian peaks (descriptive of optical resonances) and is applied to optimize computation time.
Original languageEnglish
JournalComputer Physics Communications
Pages (from-to)268-273
Publication statusPublished - 2019


  • Adaptive
  • Lorentzian
  • RCWA
  • Resolution


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